Galaxy for virologist training Exercise 2: Quality control and trimming

Despite the improvement of sequencing methods, there is no error-free technique. A correct measuring of the sequencing quality is essential for identifying problems in the sequencing, thus, this must be the first step in every sequencing analysis. Once the quality control is finished, it's important to remove those low quality reads, or short reads, for which a trimming step is mandatory. After the trimming step it is recommended to perform a new quality control step to be sure that trimming worked.

1. Illumina Quality control and trimming

1. Quality control

To run the quality control over the samples, follow these steps: 1. Create a new history, as we explained yesterday named Illumina preprocessing 2. Upload data as seen yesterday, copy and paste the following URLs:

ftp://ftp.sra.ebi.ac.uk/vol1/fastq/ERR531/002/ERR5310322/ERR5310322_1.fastq.gz
ftp://ftp.sra.ebi.ac.uk/vol1/fastq/ERR531/002/ERR5310322/ERR5310322_2.fastq.gz

1. Search for the fastqc tool and select FastQC Read Quality reports and set the following parameters:
   • Select multiple file data set in Raw read data from your current history
   • With the Ctrl key pressed, select the two datasets
   • Then go down and select Execute

To see the results we are going to open the jobs with Web page in their name for both data 1 and data 2.

Here, you can see the number of reads in each file, the maximum and minimum length of all reads in the sample, and the quality plots for both R1 and R2. They look quite good, but we are going to run trimming over the samples.

First question

2. Trimming

Once we have performed the quality control, we have to perform the quality and read length trimming:

1. Search for fastp in the tools and select fastp - fast all-in-one preprocessing for FASTQ files
2. Select custom parameters:
   • Single-end or paired reads > Paired
     • Input 1 > Browse datasets (right folder icon) > Select ERR5310322_1.fastq.gz
     • Input 2 > Browse datasets > Select ERR5310322_2.fastq.gz
   • Display Filter Options
     • Quality Filtering options
       • Qualified Quality Phred = 30
       • Unqualified percent limit = 10
     • Length Filtering Options
       • Length required = 50
   • Read modification options
     • PoliX tail trimming > Enable polyX tail trimming
     • Per read cutting by quality options
       • Cut by quality in front (5') > Yes
       • Cut by quality in tail (3') > Yes
       • Cutting mean quality = 30
3. Finally, click on Execute

To see the trimming stats, have a look at the fastp on data 2 and data 1: HTML report file. You should see something like that.

Other trimming tools

1. Search for trimmomatic in the tools and select Trimmomatic flexible read trimming tool for Illumina NGS data
2. Select custom parameters:
   • Single-end or paired-end reads? = Paired-end (two separated files)
   • Input FASTQ file (R1/first of pair) = ERR5310322_1.fastq.gz
   • Input FASTQ file (R2/second of pair) = ERR5310322_2.fastq.gz
   • Insert Trimmomatic Operation:
     • Select Trimmomatic operation to perform: MINLEN
     • Minimum length of reads to be kept = 50
3. Select Execute

Trimmomatic does not perform statistics over trimmed reads, so we need to perform FastQC again over the Trimmomatic results.

Second question

• This hands-on history URL: https://usegalaxy.eu/u/svarona/h/llumina-preprocessing

2. Nanopore Quality control and trimming

1. Quality control

To run the quality control over the samples, follow these steps: 1. Create a new history has explained yesterday named Nanopore quality 2. Upload data as seen yesterday, copy and paste the following URLs:

https://raw.githubusercontent.com/nf-core/test-datasets/viralrecon/nanopore/minion/fastq_pass/barcode01/FAO93606_pass_barcode01_7650855b_0.fastq
https://raw.githubusercontent.com/nf-core/test-datasets/viralrecon/nanopore/minion/fastq_pass/barcode01/FAO93606_pass_barcode01_7650855b_1.fastq
https://raw.githubusercontent.com/nf-core/test-datasets/viralrecon/nanopore/minion/fastq_pass/barcode01/FAO93606_pass_barcode01_7650855b_2.fastq

1. Search for the Nanoplot tool and select NanoPlot Plotting suite for Oxford Nanopore sequencing data and alignments
2. Run the tool as follows:
   • In Select multifile mode: Combined (as we are working with 3 different fastq files for the same sample, we can analyze them in batch)
   • In the files part, use Ctrl to select the three fastq files.
   • Select Execute

Now we are going to have a look to the results.

1. Select the :eye: icon in the NanoPlot on data 3, data 2, and data 1: HTML report result.
2. Have a look to the stats.

As you can see, the Mean read length is around 500 nt, which makes sense because we are using amplicon sequencing data.

First question

2. Trimming

When Nanopore reads are being sequenced, the MinKnown software splits Fast5 reads into quality pass and quality fail. As we will select only Fast5 pass reads, we won't need to perform a quality trimming, so even if we see that the reads have a bad Phred score, we know that the ONT software considered the reads as "good quality".

Then we will only be performing a read length trimming. As we are using amplicon sequencing data, we won't be expecting reads smaller than 400 nucleotides, nor higher than 600, which would obviously correspond to chimeric reads.

1. Search for artic tool
2. Select ARTIC guppyplex Filter Nanopore reads by read length and (optionally) quality
3. While pressing the Ctrl key, select the three samples
4. Remove reads longer than = 600
5. Remove reads shorter than = 400
6. Do not filter on quality score (speeds up processing) = Yes (we had already select pass reads)

We will come across one error in this job:

This happens because Galaxy does not have the software to filter SARS-CoV-2 amplicon data properly installed in their server, which is something typical that we can find in Galaxy.

• This hands-on history URL: https://usegalaxy.eu/u/svarona/h/nanopore-quality